scholarly journals SOC estimation algorithm of power lithium battery based on AFSA-BP neural network

2020 ◽  
Vol 2020 (13) ◽  
pp. 535-539 ◽  
Author(s):  
Qiuxia Wang ◽  
Peizhou Wu ◽  
Jialing Lian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Lithium Battery ◽  
Estimation Algorithm ◽  
Soc Estimation

scholarly journals SoC Estimation of Lithium Battery Based on Improved BP Neural Network

2017 ◽  
Vol 105 ◽  
pp. 4153-4158 ◽  
Author(s):  
Yifeng Guo ◽  
Zeshuang Zhao ◽  
Limin Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Lithium Battery ◽  
Soc Estimation

scholarly journals SOC Estimation of Lithium Battery Based on IPSO-BP Neural Network

2020 ◽  
Vol 1684 ◽  
pp. 012152
Author(s):  
Xinjian Mao ◽  
Shaojing Song ◽  
Feng Ding ◽  
Pan Tang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Lithium Battery ◽  
Soc Estimation

scholarly journals SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

10.29007/m89x ◽  
2020 ◽  
Author(s):  
Jong Hyun Lee ◽  
Hyun Sil Kim ◽  
In Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Discharge Voltage ◽  
State Of Charge ◽  
State Of Health ◽  
Soc Estimation ◽  
Multilayer Neural Networks

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.

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